Success stories

Learn about our projects: what objectives were set, what challenges we faced and what were the results obtained.

Success stories

Here are some clients who trust us for their digital transformation.

Here are some clients who trust us for their digital transformation.

We optimize the decision making process, the creation and implementation of marketing campaigns through automation and AI analysis.

Logistics

We optimize operating costs through the selection of the best routes, times and movements.

Smart Fishing

We use AI models to monitor fish populations, predict their growth and even their migratory patterns.

HR

Get to know your team's perceptions in depth to develop tailored action plans and optimize your results.

Banking

We leverage big data processing to segment customers, prevent fraud, generate insights and develop business strategies.

Health

Together with prestigious institutions in the healthcare sector, promoting research and the improvement of quality of life through the use of AI.

LegalTech

We create software specialized in legal terminology. Beyond digitizing documents, we classify information optimally.

CTR PREDICTION

OBJECTIVES

In 2017, one of the world’s leading automobile manufacturers and the largest carmaker in Europe, needed to measure the return on investment (ROI) and predict the performance of data and campaigns to maximize advertising spend by predicting Click Through Ratio (CTR).

CHALLENGES

Design a very efficient model to exploit the large amount of data (150M impressions - 2 months of campaign), structured as categorical variables, with very unbalanced classes.

RESULTS

Creation of a personalized and predictive CTR model with adservers data, analyzing more than 100 million monthly interactions between users and ads with F1-score metrics: 70%.

Cognitive basis for recommending an ad in a precision marketing context.

SALES PREDICTION

OBJECTIVES

In 2018, the leading global foodservice retailer, with sales of more than €950 M in Spain, wanted to improve predictive marketing accuracy and specifically, measure return on investment (ROI) by predicting weekly sales of its franchises (>420), and maximize advertising campaigns by controlling investment by channel.

CHALLENGES

Large amounts of unstructured data from different sources (4 years of sales, advertising campaigns, weather, social and sporting events, vacations, etc.).

RESULTS

Decision support system that allows forecasting sales for the coming weeks with 96% accuracy, based on environmental variables and investment levels in different advertising channels.

Identification of the weight of each channel in sales (Simulation of counterfactuals) that maximizes the return of advertising campaigns, helping to decide the allocation by channels and scheduling over time.

SMART ROUTE PREDICTION

OBJECTIVES

Satlink, a leading global provider of technology solutions for the fishing segment, wanted to add value to its marketed products by predicting, with a medium-term time horizon (7-10 days), the future trajectory of a drifting buoy and prevent it from colliding with the shore in order to increase its lifetime.

CHALLENGES

Incorporation of a large amount of daily oceanographic data (currents, wind, waves, temperature, etc.), which may fail or be delayed due to external events.

High uncertainty in weather forecasts.

Robust service deployment, used daily by multiple customers.

RESULTS

Adding value to your marketed products with ocean information and increasing buoy life by 186%.

Information monitoring alert system that allows multiple daily attempts to try to recover from incidents, with multiple concurrent requests.

PREDICTION OF THE QUANTITY OF FISH AT THE BEACON

OBJECTIVES

Fishing prediction from echo sounder signals and oceanographic data from beacons.

Distinguir marcaciones que permitan discriminar entre las especies de atúnidos más frecuentemente capturados.

CHALLENGES

Large amount of data to feed the models: beacon position information (latitude, longitude, ocean), echosounder (individual layer tags) and surface oceanography (~90 million tags from ~20k beacons over 4 years).

RESULTS

Fishing detection with 95% accuracy.

Development of classification models to predict the presence/absence of tuna under the beacon and regression models to predict the exact total amount of tons of tuna under the beacon.

ELECTRONIC MONITORING

OBJECTIVES

Satlink currently has an electronic monitoring system for fishing vessels with video cameras that record the relevant activity on deck. The recordings are processed with basic computer vision algorithms to detect the different fish species and facilitate the task of the experts when reviewing the large amount of hours of video.

CHALLENGES

Lack of labeled videos to solve the object tracking problem. This makes it complicated to train specific models that can take into account the temporal component.

RESULTS

Development and implementation of the most appropriate tracking algorithm, taking advantage of existing object detection algorithms. Reduction in the number of false positives by taking into account the temporal component. The developed algorithms also serve as assistants for tagging new videos, if necessary. 

CUSTOMERS CLUSTERIZATION

OBJECTIVES

A bank with more than 3 million customers wanted to segment its customers based on their bank card transactions and according to five areas (how, where, when, when, how much, what) and to chart trends in the evolution of consumption patterns.

CHALLENGES

Large volume of data with over 3 million customers with a number of transactions semi-annually around, ~190M banking transactions, with ~40 columns, need for business sense cluster interpretation.

RESULTS

Scalable clustering models that serve as a cognitive foundation and input to the bank's other models: income prediction, propensity to consume, and customer and merchant similarity measures.

Build customer loyalty by offering more personalized products and services and selecting the right marketing channels for each audience.

CREDIT CARD FRAUD DETECTION

OBJECTIVES

Given a bank card transaction, develop a system to manage alerts and real-time prediction of whether it is fraudulent or legitimate.

Development of a decision support system to manage alerts. That is, deciding when to alert or suspend an operation taking into account all the costs involved.

CHALLENGES

Large data set (+150 million payment transactions). Need to perform model inference in real time ( in order to avoid customer lag at checkout).

Need to perform model inference in real time (so that the customer does not perceive a delay when paying). 

RESULTS

36% improvement over previous accuracy.

Fraud classification models improving accuracy and keeping false positive rate (customer inconvenience) under control.

With the information gathered from the project data, a study was conducted on the behavior of the fraudsters. 

STRUCTURING OF BREAST CANCER REPORTS

OBJECTIVES

Development, implementation and deployment of several Natural Language Processing (NLP) models aimed at structuring and coding clinical reports of breast cancer biopsies in SNOMED terminology, that would allow the Hospital Puerta del Mar de Cádiz to save time of its physicians and increase the productivity of the Anatomic Pathology service.

CHALLENGES
Large amount of information stored as free text in clinical reports (>150k reports).

Unstructured language reports with multiple classes (400+).
RESULTS

Development of two independent coding models (based on SNOMED-CT terminology) and integration of the models with speech recognition.

  • Saved the time of its physicians and increased the productivity of the Anatomic Pathology service.
  • Multi-class classifier with 86% accuracy and 87% F1 for more than 400 classes.
  • NER: 90% F1 score with 4 different entities.
  • CARDIOVASCULAR DISEASES STUDY

    OBJECTIVES

    The Instituto de Salud Carlos III together with Quirón Prevención, which provided the anonymous data used in this study and supported the analyses and interpretation, wished to determine the influence of different factors on the risk of cardiovascular disease.

    CHALLENGES

    Maintain the highest scientific standards to be accepted in prestigious journals; using non-standardized data from many sources; with more than 500,000 patients with multiple medical recognitions over the years. 

    RESULTS

    Publication of study results in the European Journal of Preventive Cardiology. Through the homogenization of textual information, evidence is provided on how physical activities can reduce the risk of serious diseases.

    NLP IN END-TO-END LEGAL TEXTS

    OBJECTIVES

    In 2018, the leading company in Spain in the provision of legal information, wanted to classify, tag and identify key data automatically from a large corpus (1M+) of constantly growing judgments and legal documents and be able to manage the search needs on these documents for decision making.

    CHALLENGES

    Start with unlabeled data, which given the very specific legal and Spanish language context makes the use of pre-trained tools impossible. Requires use of annotation tools and active learning strategy and SOTA tools in 2018.

    RESULTS

    Classification system of judgments and documents according to legal categories organized by numerous hierarchical classification levels with an accuracy of more than 90%.

    Semantic search is 22 times more efficient than the previous solution, which significantly reduces operational time and increases service productivity.

    SENTIMENT ANALYSIS

    OBJECTIVES

    The client in the digital sector wanted to develop a tool that would allow them to collect texts from any source to analyze the information, understand it and take action on the work environment and monitor the opinion, emotion or attitude of employees.

    CHALLENGES

    Need for robust model with high accuracy in order to increase the % of correctly classified texts without adding computational cost.

    RESULTS

    Development of a multi-label text classification and sentiment analysis model with 96% accuracy that helps to adopt new strategies and make decisions in an agile way. Use of state of the art models in NLP (Transformers): contribution to Hugging Face. Active learning techniques: zero-shot learning, entropy-based intelligent labeling.

    ROUTE OPTIMIZATION SYSTEM

    OBJECTIVES

    Analysis and optimization of the routing system to provide it with more flexibility, modularity and maintainability.

    In addition, the previous system was to be upgraded by correcting design errors and improving performance.

    CHALLENGES

    Code not very maintainable and difficult to understand as it is not documented and absence of a global vision of the optimization problem to be solved from a mathematical point of view.

    RESULTS

    New formulation of the mathematical problem for route management that allows adapting to new scenarios and requirements as the product is implemented in each country.

    Improvement of the route optimization system to minimize operational costs, planning time, fuel use and pollution, improving traceability and fleet management.

    How to apply them
    to your business

    How to apply them
    to your business